Key Features
-
🔌 SDK: Comprehensive Python SDK (
simba-sdk) for easy integration -
🧩 Modular Architecture: Flexible integration of
vector stores,embedding models,chunkers, andparsers - 🖥️ Modern UI: User-friendly interface for managing document chunks and monitoring system performance
- 🔗 Seamless Integration: Effortlessly connects with any RAG-based system
- 👨💻 Developer-Centric: Simplifies complex knowledge management tasks
- 📦 Open Source & Extensible: Community-driven with extensive customization options
System Architecture Overview
Simba employs a modular architecture with these key components:- Document Parsers: Parse and extract content from various document formats
- Chunkers: Divide documents into semantically meaningful segments
- Embedding Models: Convert text into vector representations
- Vector Stores: Index and store embeddings for efficient retrieval
- Retrieval Engine: Find relevant information using various retrieval strategies
- API Layer: Expose functionality through a RESTful interface
- SDK: Provide programmatic access to all functionality
Demo

Who is Simba for?
Simba is ideal for:- AI Engineers: Building RAG applications that require contextual knowledge
- Developers: Creating context-aware applications with minimal boilerplate
- Organizations: Seeking to leverage their internal knowledge for AI applications
Deployment Options
Simba offers two primary deployment models to suit different organizational needs:Cloud
Get started using Simba through our Cloud offering, free of charge.
Perfect for fast serverless deployment.
Self Hosted
Host your own full-featured Simba system. Ideal for on premise use cases.
Complete control over your data & infra.

